Podcasts about Schelling

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

How can you create an HR Business Partner Center of Excellence?Why does HR need to shift from owning programs to owning business outcomes?My guest on this episode is Christina Schelling, SVP, Human Resources, VerizonDuring our conversation Christina and I discuss:Why naming the HRBP group a Center of Excellence gives partners community, shared goals, and focused development.How the HR business partner role is evolving to be more strategic.How Verizon defined HRBP success with four shared metrics aligned to their strategy.Why HRBPs don't need to be experts in everything, and how COE partners make them stronger.How Verizon partnered with their HR tech vendors co-create innovative AI talent solutions.Connecting with Christina SchellingConnect with Christina Schelling on LinkedInEpisode Sponsor:Next-Gen HR Accelerator - Learn more about this best-in-class leadership development program for next-gen HR leadersHR Leader's Blueprint - 18 pages of real-world advice from 100+ HR thought leaders. Simple, actionable, and proven strategies to advance your career.Succession Planning Playbook: In this focused 1-page resource, I cut through the noise to give you the vital elements that define what “great” succession planning looks like.

This week Denae tells Kim about the almost cold case of Kelsie Schelling. Thanks to a mom that heard her story stepped in and brought justice to Kelsie and her family.Sources:Colorado Cold Case Files - Case Detail: Kelsie SchellingOxygen.com Donthe Lucas Kills Kelsie Schelling, Mom of His Unborn Baby by Jill SederstromABC7news.com Mom fights to bring pregnant daughter Kelsie Schelling's killer to justice 8 years after she disappeared ByAllie Yang, Gerry Wagschal, Emily Wynn, and Joseph Rhee 20/20 S44 ep 19 Finding Kelsie l 20/20 l PART 1 – 5Video Woman uncovers crucial information in Kelsie Schelling case: Part 7 - ABC Newskoaa.com District Attorney says murdered witness was going to provide bombshell testimonyThe Pueblo Cheiftan Donthe Lucas loses appeal in Kelsie Schelling Colorado murder case by Justin Reuter Former jail inmate says Donthe Lucas claimed Kelsie Schelling's body would never be found | KRDO by Andrew McMillan and Dan BeedieFind us on Instagram at: Mysterious Mavens Podour Gmail: mysteriousmavenspod@gmail.comNot to braaaaag but our website: mysteriousmavenspod.comPlease rate us 5 stars!!!and as always SELF CARE FOR YOURSELF!!!

Die Brandkatastrophen in Europa sind historisch: Feuerwehr und Rettungskräfte befinden sich im Dauereinsatz. Was die Lage zusätzlich erschwert: Neue Löschflugzeuge sind kaum verfügbar. Die kleinen Propellermaschinen fliegen extrem tief und können ihre Wassertanks in Seen und Flüssen füllen. Das Modell Canadair 515 etwa gilt als Klassiker unter den Löschflugzeugen: In nur zwölf Sekunden ist der Wassertank voll, und die Maschine kann zum Brand zurückfliegen. Solche Maschinen sind derzeit auch bei den Waldbränden in Spanien und Frankreich im Einsatz. Doch die Flotten der bewährten Canadair-Flieger sind knapp, da der Hersteller die Produktion jahrelang ausgesetzt hatte. Selbst bei neuen Bestellungen dauert es Jahre, bis Maschinen und geschultes Personal einsatzbereit sind. In dieser Episode beleuchten wir, warum die grenzüberschreitende Hilfe der EU an ihre Grenzen stösst. Wir sprechen über die besonderen Herausforderungen der Piloten beim Löscheinsatz und zeigen auf, warum neben der Verstärkung aus der Luft vor allem Massnahmen am Boden entscheidend sind. Heutiger gast: Jürgen Schelling, Aviatik-Journalist Host: Alice Grosjean Redaktion: Simon Schaffer Mehr Informationen: [Überblick zu den Waldbränden in Europa ](https://www.nzz.ch/panorama/fast-eine-halbe-million-menschen-sind-von-flammen-bedroht-so-ist-die-lage-in-spanien-frankreich-und-italien-ld.10017137)in der NZZ. [Interaktive Temperaturkarte](https://www.nzz.ch/visuals/hitzewelle-in-europa-die-interaktive-temperatur-karte-im-live-check-ld.10012978) zur Hitze in Europa. [Jürgens NZZ-Artikel z](https://www.nzz.ch/mobilitaet/waldbraende-in-europa-neue-loeschflugzeuge-sind-kaum-verfuegbar-ld.10016896)um Mangel an Löschflugzeugen. [Exklusiv für dich als Podcast-Fan: 7 Tage NZZ-Digitalabo geschenkt. Unverbindlich testen. ](https://probeabo.nzz.ch/podcast?utm_source=shownotes&utm_medium=podcast&utm_campaign=0726_NZZCH_Podcast)

When 21-year-old Kelsie Schelling drove to Pueblo to meet the father of her unborn child, she vanished without a trace. Her body has never been found, but a trail of lies, surveillance footage, and one ordinary woman's extraordinary courage would help bring her killer to justice. This is the heartbreaking story of Kelsie Schelling—and Lauren Suhr, the stranger who risked everything to uncover the truth. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In this unlocked patreon episode Breht traces and explains a subterranean philosophical lineage running from Spinoza through Hegel to Marx and Louis Althusser. Beginning with Spinoza's conception of God or Nature as a single, immanent Substance, he explores how Hegel transforms Substance into a self-developing Subject, how Marx grounds the dialectic in material social life and class struggle, and how Althusser attempts to remove the final hidden Subject from Marxist theory. Along the way, he examines the concept of immanent critique, Kant's transcendental idealism, Hegel's master-slave dialectic, Marx's materialist transformation of idealism, and Althusser's concepts of overdetermination, interpellation, theoretical anti-humanism, and history as a "process without a Subject." What emerges is an ongoing struggle over immanence, contradiction, human agency, and whether history possesses either a sovereign author or a guaranteed destination. Listen to these related episodes to learn more: Economics and Philosophical manuscripts of 1844 Louis Althusser: Ideology and Ideological State Apparatuses The Nature of All Things: Spinoza's Philosophical Odyssey Intro to German Idealism: Kant, Fichte, Schelling, & Hegel Hegelian Dialectics: Contradiction, Marxism, & the Freudian Unconscious   Check out our new merch designs and support the show HERE Learn more here: https://revleftradio.com/

In this unlocked patreon episode Breht traces and explains a subterranean philosophical lineage running from Spinoza through Hegel to Marx and Louis Althusser. Beginning with Spinoza's conception of God or Nature as a single, immanent Substance, he explores how Hegel transforms Substance into a self-developing Subject, how Marx grounds the dialectic in material social life and class struggle, and how Althusser attempts to remove the final hidden Subject from Marxist theory. Along the way, he examines the concept of immanent critique, Kant's transcendental idealism, Hegel's master-slave dialectic, Marx's materialist transformation of idealism, and Althusser's concepts of overdetermination, interpellation, theoretical anti-humanism, and history as a "process without a Subject." What emerges is an ongoing struggle over immanence, contradiction, human agency, and whether history possesses either a sovereign author or a guaranteed destination. Listen to these related episodes to learn more: Economics and Philosophical manuscripts of 1844 Louis Althusser: Ideology and Ideological State Apparatuses The Nature of All Things: Spinoza's Philosophical Odyssey Intro to German Idealism: Kant, Fichte, Schelling, & Hegel Hegelian Dialectics: Contradiction, Marxism, & the Freudian Unconscious   Check out our new merch designs and support the show HERE Learn more here: https://revleftradio.com/  

Send us Fan MailWe start with a simple mystery: how the same person can make a proud plan at night and break it in the morning, even while still believing the plan is good. We connect akrasia, free will, and moral responsibility to transaction costs, treating self-control as a contracting problem between competing versions of ourselves. • Jordan I and Jordan II as a model of divided preferences over time • Hard determinism, libertarian free will, and compatibilism as the main philosophical map • Responsibility and punishment as social conventions that reduce coordination costs • Aristotle on voluntary intoxication and why blame can attach upstream • Akrasia defined as wanting to want the right thing • Plato's knowledge-based denial of akrasia versus Aristotle's acceptance of weakness of will • Paul and Augustine on the internal conflict of the will • Time-inconsistent preferences and hyperbolic discounting as a behavioral explanation • Commitment devices as credible commitments and transaction cost engineering • Odysseus and the sirens as the classic precommitment story • Listener letter on military punctuality as costly signaling and institutional discipline • Listener letter on recycling labels as regulation creating new transaction costs Links and Sources1. Ainslie, G. (1992). Picoeconomics: The strategic interaction of successive motivational states within the person. Cambridge University Press.  Link: Cambridge University Press — Picoeconomics 2. Aristotle. (1999). Nicomachean ethics (W. D. Ross, Trans.). Batoche Books. (Original work published ca. 350 B.C.E.)  Passage: Book VII (on incontinence/akrasia). Link: Nicomachean Ethics, trans. W. D. Ross (free PDF)3. Augustine. (1838). The confessions of Saint Augustine (E. B. Pusey, Trans.). John Henry Parker. (Original work written ca. 397–400 C.E.)  Passage: Book VIII, Chapters 8–9 (§§20–21). Link: Confessions, Book VIII, Pusey translation (free full text)4. Davidson, D. (1970). How is weakness of the will possible? In J. Feinberg (Ed.), Moral concepts (pp. 93–113). Oxford University Press. Link: Oxford Academic — "How Is Weakness of the Will Possible?" (in Essays on Actions and Events) (gated — subscription/institutional access)5. Dennett, D. C. (1984). Elbow room: The varieties of free will worth wanting. MIT Press. Link: MIT Press — Elbow Room (gated — purchase; excerpts free)6. Dennett, D. C. (2003). Freedom evolves. Viking.  Link: Internet Archive — Freedom Evolves (gated — free with Internet Archive lending account)7. Hume, D. (1999). An enquiry concerning human understanding (T. L. Beauchamp, Ed.). Oxford University Press. (Original work published 1748) Passage: Section VIII, "Of Liberty and Necessity." Link: Hume Texts Online — Section 8 (free full text)8. King James Bible. (2017). King James Bible Online. https://www.kingjamesbibleonline.org/ (Original work published 1769) Passage: Romans 7. Link: Romans, Chapter 7 (KJV) (free full text)9. Plato. (1967). Protagoras (W. R. M. Lamb, Trans.). Harvard University Press. (Original work published ca. 380 B.C.E.)  Link: Perseus Digital Library — Protagoras (Lamb trans.) (free full text)10. Schelling, T. C. (1984). The intimate contest for self-command. In Choice and consequence: Perspectives of an errant economist. Harvard University Press.  Link: National Affairs — "The Intimate Contest for Self-Command" (original 1980 essay, free full text) · Harvard University Press — Choice and Consequence (book page, gated)11. Strawson, P. F. (1962). Freedom and resentment. Proceedings of the British Academy, 48, 1–25. Link: The British Academy — Freedom and Resentment (official publisher page, may be gated)12. Vaihinger, H. (1925). The philosophy of 'as if': A system of the theoretical, practical and religious fictions of mankind (C. K. Ogden, Trans.). Harcourt, Brace. (Original work published 1911)Link: Internet Archive — The Philosophy of 'As If' (free full text)Book-o-da-Week:  Emily Wilson's translation of Homer's THE ODYSSEY, 2018, WW Norton. If you have questions or comments, or want to suggest a future topic, email the show at taitc.email@gmail.com !You can follow Mike Munger on Twitter at @mungowitz 

As a brutal 105-day conflict between the United States and Iran gives way to a fragile ceasefire, are we witnessing the birth of a true Nash equilibrium or merely a temporary pause born from exhaustion? In this episode of The Valley Current®, Jack Russo examines a newly brokered agreement that reopens the Strait of Hormuz and halts hostilities for sixty days, while leaving the central question of Iran's enriched uranium stockpile unresolved. Drawing on game theory, Jack explores the difference between a self-enforcing Nash equilibrium and a Schelling focal point held together only by immediate pressure and mutual desperation. He also examines the destabilizing influence of regional actors operating outside the agreement and questions whether this expensive diplomatic arrangement is ultimately less durable and less verifiable than the 2015 JCPOA. With the most consequential issues still unresolved, the real test is not whether this ceasefire can hold for sixty days, but whether it can survive the first serious challenge. Jack Russo Managing Partner Jrusso@computerlaw.com www.computerlaw.com https://www.linkedin.com/in/jackrusso "Every Entrepreneur Imagines a Better World"®️  

Schlaf - getrackt. Essen - fotografiert und hochgeladen. Herzfrequenz - im Blick. Fortschritte beim Fitness - in der App dokumentiert. Selbstoptimierung des Körpers ist ein Trend. Doch: Wozu? Mit Hausarzt Prof. Jörg Schelling.

Our card this week is Kelsie Schelling, the 10 of Spades from Colorado. In February 2013, 21-year-old Kelsie Schelling had just learned she was eight weeks pregnant. Hours after her first prenatal appointment, she drove from Denver to Pueblo -  and was never seen again. Kelsie's body has never been found. But years later, someone was convicted of her murder. How do investigators build a case without a body? And after more than a decade, is there still a chance of finding Kelsie? If you have any information, contact the Colorado Bureau of Investigation cold case line at 303-239-4244 or Pueblo Crime Stoppers at 719-542-7867. View source material and photos for this episode at: https://thedeckpodcast.com/kelsie-schelling   Let us deal you in… follow The Deck on social media. Instagram: @thedeckpodcast | @audiochuck Twitter: @thedeckpodcast_ | @audiochuck Facebook: /TheDeckPodcast | /audiochuckllc To support Season of Justice and learn more, please visit seasonofjustice.org. The Deck is hosted by Ashley Flowers.  Instagram: @ashleyflowers TikTok: @ashleyflowerscrimejunkie Twitter: @Ash_Flowers Facebook: /AshleyFlowers.AF Text Ashley at 317-733-7485 to talk all things true crime, get behind the scenes updates, and more! Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Unser Gesundheitswesen ist eines der teuersten der Welt. Aber das Geld wird nicht effizient eingesetzt. Zum Beispiel gibt es Überversorgung. Schadet das Patienten und Patientinnen? Erfahrungen von Hausarzt Prof. Jörg Schelling.

Marco Filoni"La Russia e l'Occidente"Fëdor TjutčevAdelphiwww.adelphi.itUn libro che parla di geopolitica molto prima che questa avesse un nome – indispensabile per capire la Russia di oggi alla luce di quella di quasi due secoli fa.A cura di Marco FiloniCon un saggio di Massimo Cacciari«Per la prima volta in Europa si levò la voce ferma e coraggiosa dell'opinione pubblica russa». Con queste lapidarie parole lo scrittore Ivan Sergeevič Aksakov accolse una serie di articoli apparsi in Germania e in Francia sul finire degli anni Quaranta dell'Ottocento e destinati a suscitare una vasta eco in Occidente. L'autore di quelle pagine anonime, che osavano rivolgersi all'Europa con inaudita libertà e dignità, era Fëdor Tjutčev. Diplomatico, poeta ammirato da Puškin e da Turgenev, da Dostoevskij e da Tolstoj, uomo di grandi vizi e virtù, Tjutčev era animato da un entusiasmo senza limiti per la sua Russia, che – credeva fermamente – sarebbe diventata un grande impero, capace di unire tutti i popoli slavi di fede ortodossa. Ancora oggi, se si vogliono comprendere le mire espansionistiche di quel paese, è agli scritti politici di Tjutčev che occorre volgere lo sguardo. Fra le sue «intuizioni storiche» – come le definisce il teologo Georgij Florovskij –, spiccano l'agonia della civiltà occidentale, la questione romana e il Papato, il ruolo della censura e dell'autocrazia zarista, fino alla previsione di una catastrofica guerra che l'Occidente avrebbe scatenato contro la Russia uscendone sconfitto, e che avrebbe segnato l'inizio di un nuovo capitolo della Storia. Temi, come salta agli occhi, di bruciante attualità.Appartenente a una famiglia dell'aristocrazia moscovita, Fëdor Ivanovič Tjutčev (1803-1873) fu diplomatico oltre che eminente poeta, e dopo aver iniziato la carriera nel Collegio degli Affari esteri di Pietroburgo operò come incaricato speciale a Monaco di Baviera – dove frequentò Heine, Schelling e gli ambienti del Romanticismo tedesco – e a Torino, dove visse dal 1837 al 1839. Nel 1836 alcune sue liriche furono pubblicate dalla rivista di Puškin «Il contemporaneo», suscitando i primi, ampi consensi. Nel 1844 tornò definitivamente in Russia, mentre la sua fama di poeta cresceva dopo i riconoscimenti tributatigli da Turgenev, Fet, Dobroljubov.Marco Filoni insegna filosofia politica all'Università Link di Roma. Ha insegnato e svolto ricerca al Politecnico di Milano, all'Istituto di Studi Superiori dell'Università di Bologna e all'École Normale Supérieure di Parigi. Nel 2022 è stato nominato titolare del programma di ricerca Éclaireurs della Fondation Robert de Sorbon di Parigi. Tra i suoi libri: Anatomia di un assedio. La paura nella città (Skira 2019); L'azione politica del filosofo. La vita e il pensiero di Alexandre Kojève (Bollati Boringhieri 2021); Il calcolo della paura (Einaudi 2021); Lineamenti di una fenomenologia del diritto (Marsilio 2024).Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Throughout this series we have pulled apart science by science to show how the Aryan hypothesis works. We have now reached the continent of the unconscious, with it, neurology, psychiatry and psychology etc. We are getting ready to storm the castle that is Carl Gustav Jung.We start with the "invention of the self" during the Sturm & Drang, Goethe‘s urtype and degeneration theory, introduce Schelling as the first irrationalist continue with the forbidden secrets Mesmer revealed about the ancien régime and the role he played in the french revolution.We also present and I read in full a forgotten fragment of Hölderlin, Marx' favorite poet and best friend of Hegel, who it would seem, before the Nazis deemed it a fake, first coined the term Communism, 236 years ago at a small chapel on a romantic hill at the feet of the alps.

Herpes-zoster-Impfung in der zweiten Lebenshälfte Viele ältere Menschen fühlen sich fit und jünger als sie sind: 60 ist das neue 40. Beeinflusst das die Impfempfehlungen mit Blick auf Gürtelrose? Dieser Frage geht die aktuelle Folge der Podcast-Serie „O-Ton Allgemeinmedizin Extra“ mit Professor Dr. Jörg Schelling, Martinsried, nach. Die häufigste Ursache für eine Gürtelrose ist die Schwächung des Immunsystems im Alter – auch bei Menschen, die sich jünger fühlen als das im Reisepass angegebene Alter. Darüber hinaus steigern auch chronische Erkrankungen wie Diabetes, Rheuma oder chronische Atemwegserkrankungen das Risiko an Herpes zoster (HZ) zu erkranken. Eine Gürtelrose kann die Lebensqualität erheblich beeinträchtigen Ältere und chronisch Kranke sind nicht nur häufiger von Gürtelrose betroffen, sondern sie haben auch ein höheres Risiko für schwere Verläufe und bei bis zu einem Drittel kommt es zu einer Post-Zoster-Neuralgie. Das Ausmaß mit dem die HZ-bedingten neuropathischen Schmerzen die Lebensqualität beeinträchtigen können, wird jedoch häufig unterschätzt. Die Impfung schützt Die STIKO empfiehlt eine Impfung gegen Herpes zoster für alle Personen im Alter von mindestens 60 Jahren. Menschen mit einer schweren Grunderkrankung können bereits ab einem Alter von 18 Jahren zu Lasten der GKV geimpft werden. Um die bisher unzureichende Impfquote zu steigern, empfiehlt Schelling, das Thema Impfen bei jedem Patientenkontakt anzusprechen und ggf. mehr als eine Impfung bei einem Termin zu verabreichen. Diese Podcast-Episode ist mit freundlicher Unterstützung von GSK entstanden. https://bit.ly/4hoYfbK

Stefano Poggi"Il mito dell'istante"I filosofi davanti al tempo: da Schelling a DerridaAnders Solferinowww.solferinolibri.itLa riflessione sul problema del tempo è uno dei temi fondamentali della tradizione filosofica occidentale.Ma gli ultimi due secoli – l'Ottocento e il Novecento – ne hanno visto una profonda trasformazione. Gli sviluppi dell'indagine scientifica si sono intrecciati con la maturazione di una inedita concezione della soggettività e della coscienza. Sono state riprese e affrontate con nuovi occhi questioni antichissime, in primo luogo quella già posta da sant'Agostino («Cos'è davvero il tempo? Lo so, ma non lo so spiegare »). Ci si è interrogati sui modi in cui il tempo viene vissuto, misurato, narrato, condiviso. È così apparso con sempre maggior chiarezza che il tempo è la realtà dello stesso nostro esistere, che il tempo – come scrive Borges – «è la sostanza di cui sono fatto». Per questo, del tempo, parliamo sempre come di un divenire, di un fluire. Un divenire, un fluire apparso non di rado come una successione di istanti. Istanti in cui fermare il tempo, arrestarlo nell'attimo «così bello» del Faust, riuscire in un'impresa che però, di fronte ai nuovi saperi scientifici, appare destinata a ridursi in speranza, a rivelarsi un'illusione. L'istante non esiste. E, se esiste, forse altro non può essere che l'eternità.Partendo da Hegel e Schelling per arrivare a Bergson, Russell, Heidegger e senza dimenticare i grandi «narratori del tempo» come Proust e Joyce, Stefano Poggi racconta gli episodi di una storia che non ci è presente in tutta la sua decisiva importanza perché è spesso sotterranea, ma che ha inciso nel profondo sulla nostra stessa identità di uomini moderni.Stefano Poggi ha insegnato Storia della filosofia all'Università di Firenze, è stato presidente della Società Filosofica Italiana e ha diretto «Intersezioni. Rivista di storia delle idee». Tra i suoi numerosi libri ricordiamo: La logica, la mistica, il nulla (2006), La vera storia della Regina di Biancaneve (2007), I viaggi dei filosofi (2010), L'io dei filosofi e l'io dei narratori (2011), L'anima e il cristallo (2014), Il colore e l'ombra (2019), Individuo e destino (2025).Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Wer je einen Schlaganfall erlitten hat, weiß: Das darf kein 2. Mal passieren. Was kann Reha leisten und was kann man selbst zur Vorsorge beitragen? Die Internistin Dr. Wajima Safi und der Hausarzt Prof. Jörg Schelling antworten.

Today on Scope Conditions: when the bombs don't go off, the war isn't over.We tend to think of peace as beginning when the bombs stop falling. But as our guest today shows us, this is only half the story. Over the course of the Vietnam War, the United States engaged in massive bombing in Cambodia. Between 1965 and 1973, the U.S. dropped 500,000 tons of explosives there — more than the combined weight of every man, woman, and child in the country. Dr. Erin Lin, an Associate Professor of Political Science at the Ohio State University, set out to understand the continued impacts of this cataclysmic bombing campaign on Cambodian society. A landmark 2011 study had given us a partial answer: it had concluded that US bombing had no measurable long-term effects on economic outcomes in Southeast Asia. For years, that finding set the terms of the debate.In her award-winning book, When the Bombs Stopped: The Legacy of War in Rural Cambodia, published by Princeton University Press, Erin pushes back. She argues that those analyses were looking at the wrong level — that district-level aggregates conceal devastating effects on individual households and farms. More than that, they were looking at only half the intervention. It's the bombs that didn't detonate — an estimated 26 million cluster munitions still embedded in the soil — that are shaping life today in rural Cambodia.Erin spent years farming alongside families, combing through declassified military records, and building some of the most granular data ever assembled on the American bombing campaign. Her creative multi-method research design allows her to trace the dramatic long-term consequences of unexploded ordinance for the economic livelihood of Cambodian farmers.We talk with Erin about the many ironies laced through her findings: that cluster munitions are most likely to fail in soft, fertile soil, meaning Cambodia's most agriculturally valuable land is also its most contaminated; that bomb contamination can paradoxically shield farmers from predatory land seizures by political elites; and that unexploded ordnance, rather than forging solidarity among those living with it, tends to deepen ethnic divisions within villages.We hope you learn from this conversation. To stay informed about future episodes, follow us on X and Bluesky @scopeconditions and check out our website, scopeconditionspodcast.com, where you can also find references to all the academic works we discuss. And if you like the show, please rate and review us on Apple Podcasts or Spotify.We note that we recorded this interview before the recent US-Israeli war with Iran. Now, here's our conversation with Erin Lin.Works cited in this episodeBiddle, Steven. 2004. Military Power: Explaining Victory and Defeat in Modern Battle. Princeton University Press.Brooks, Rosa. 2014. “Cross-Border Targeted Killings: ‘Lawful but Awful'?” Harvard Journal of Law and Public Policy 38:233–50.________. 2014. “Drones and the International Rule of Law.” Ethics & International Affairs 28(1):83–103. ________. 2016. How Everything Became War and the Military Became Everything: Tales from the Pentagon. Simon and Schuster.Horowitz, Michael C. 2010. The Diffusion of Military Power. Princeton University Press.Lyall, Jason, and Isaiah Wilson. 2009. “Rage against the Machines: Explaining Outcomes in Counterinsurgency Wars.” International Organization 63(1):67–106.Reiter, Dan, and Allan C. Stam. 2010. Democracies at War. Princeton University Press.Pape, Robert A. 2014. Bombing to Win: Air Power and Coercion in War. Cornell University Press.Schelling, Thomas. 2008. Arms and Influence. Yale University Press.Sheehan, Neil. 1971. “Should We Have War Crime Trials?” New York Times Book Review. 

Chris joined me for a conversation on Friedrich Schelling & German Idealism! In spite of his prominence, Schelling tends to be underdiscussed in popular philosophy circles when it comes to the German Idealist tradition. In this episode, we talk about his essay Philosophical Inquiries into the Essence of Human Freedom, the dialectic of potencies that develops out of nature-philosophy, and the relation of Schelling's ideas to those of his school friends at Tubingen - two gentlemen you may or may not have heard of, named Hegel and Holderlin. The three of them were enthusiastic about the French Revolution, and planted a "freedom tree", around which they danced and sang "Hen Kai Pain" - "One and All" - the watchword of Hellenistic pantheists. Schelling's late lectures were attended by everyone from Kierkegaard to Burckhardt to Engels to Bakunin; his views on myth (centering on Apollo and Dionysus) likely influenced Nietzsche, and his notion of the dark ground as a ceaseless impulsive striving echoes in the work of Schopenhauer. At the end of the episode, we have a brief discussion about Chris' thoughts on Deleuze, a philosopher he has drifted away from, and some of the pitfalls of post-structuralist thinking.Christopher, on how to read Schelling's Freedom Essay: https://epochemagazine.org/77/freedom-god-and-ground-an-introduction-to-schellings-1809-freedom-essay/Papers Referenced: Exceeding Reason: Freedom and Religion in Schelling and Nietzsche by Dennis Vanden AuweeleNietzsche, German Idealism and Its Critics (DeGruyter)

Grippe, Corona, Gürtelrose - wogegen soll man sich impfen lassen? Und was ist mit Tetanus, Keuchhusten etc. - braucht man die als erwachsener Mensch noch? - Prof. Jörg Schelling informiert über Impfungen für Erwachsene.

Romanticism gets treated like a synonym for nostalgia, and German Idealism gets shrunk to a few brand-name thinkers. We push back on both habits by talking with Christopher Satoor, a York University doctoral candidate and founder of the Young Idealist series, about what really happens when philosophy, poetry, art, and science collide in Jena.Schelling sits at the center of that collision. We dig into why his Naturphilosophie is neither “woo” nor a quaint premodern science lesson, but a serious attempt to rebuild our concept of nature after Cartesian mechanism. That means thinking in terms of living processes, hidden forces, and organic organization, and then asking what it does to our view of mind, creativity, and embodiment when “nature is visible spirit and spirit is invisible nature.” Along the way, we unpack the rift with Fichte, the shadow cast by Hegel, and how later caricatures and missing translations shaped Schelling's reputation in English-language philosophy.We also take the political and ethical questions seriously: what the Freedom Essay contributes to debates about evil, freedom, and the limits of purely dialectical stories of progress, and why Schelling's later “positive philosophy” focuses on existence, facticity, and the question of why there is something rather than nothing. Finally, we connect the stakes to the present, where climate change and environmental catastrophe demand a less mechanized picture of the world and a more holistic way of thinking across disciplines.If you enjoy deep dives into German Romanticism, German Idealism, Schelling, Kant, Fichte, Hegel, philosophy of nature, and freedom, subscribe, share this with a friend who argues about materialism, and leave a review with the biggest idea you're still wrestling with.Send us Fan Mail Musis by Bitterlake, Used with Permission, all rights to BitterlakeSupport the showCrew:Host: C. Derick VarnIntro and Outro Music by Bitter Lake.Intro Video Design: Jason MylesArt Design: Corn and C. Derick VarnLinks and Social Media:twitter: @varnvlogblue sky: @varnvlog.bsky.socialYou can find the additional streams on YoutubeCurrent Patreon at the Sponsor Tier: Jordan Sheldon, Mark J. Matthews, Lindsay Kimbrough, RedWolf, DRV, Kenneth McKee, JY Chan, Matthew Monahan, Parzival, Adriel Mixon, Buddy Roark, Daniel Petrovic,Julian

Acquista il mio nuovo libro, “Anche Socrate qualche dubbio ce l'aveva”: https://amzn.to/3wPZfmCUltima puntata dedicata a Schelling, in cui esploriamo la sua concezione – importantissima – dell'arte.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dentro-alla-filosofia--4778244/support.

Acquista il mio nuovo libro, “Anche Socrate qualche dubbio ce l'aveva”: https://amzn.to/3wPZfmCLa storia va in una direzione chiara, secondo Schelling, sintesi di libertà e necessità. Ecco come.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dentro-alla-filosofia--4778244/support.

Francis J. Gavin, chair of the TNSR editorial board, joins us to discuss his article, "Strategic Stability and Its Limits: Reflections on Schelling." Gavin explains why Thomas Schelling remains foundational to nuclear strategy despite being an economist, and argues that "strategic stability" is often invoked without clear definition. He highlights tensions between mutual vulnerability and US extended deterrence and nonproliferation goals, and describes contradictions between Schelling's writings on arms control and coercion. Gavin critiques simplified historical lessons about surprise attack and inadvertent war shaping stability theory, traces how Cold War political constraints drove US nuclear posture, and urges policymakers to put politics and state interests first when assessing nuclear risks and emerging technologies such as AI, cyber, autonomy, and biotechnology. Hosts: Sheena Chestnut Greitens and Ryan Vest Producer: Jordan Morning

Acquista il mio nuovo libro, “Anche Socrate qualche dubbio ce l'aveva”: https://amzn.to/3wPZfmCDopo aver parlato della Natura, è il momento di parlare dello Spirito per come lo concepisce Schelling.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dentro-alla-filosofia--4778244/support.

Acquista il mio nuovo libro, “Anche Socrate qualche dubbio ce l'aveva”: https://amzn.to/3wPZfmCSchelling ci parla della natura, con tratti di grande modernità ma anche con qualche contraddizione.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dentro-alla-filosofia--4778244/support.

Acquista il mio nuovo libro, “Anche Socrate qualche dubbio ce l'aveva”: https://amzn.to/3wPZfmCIniziamo a parlare di Schelling, il secondo grande esponente dell'idealismo tedesco. E iniziamo partendo dal suo tentativo di coniugare Fichte e Spinoza.Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/dentro-alla-filosofia--4778244/support.

Send a textAs institutional investing in private markets makes its way into the wealth channel, advisors and investors are being asked to think differently about liquidity, access, and portfolio construction.In this episode, Dan sits down with Chris Schelling, Managing Director at Aksia, to explore what happens when institutional discipline meets retail reality, and how private markets are evolving as a result.Together, they discuss:Why private markets are becoming more accessible to wealth clients, and what's driving adoptionThe structural differences between institutional and retail portfolios when allocating to alternativesHow evergreen and semi-liquid vehicles work and why they are often preferable for retail investorsWhat advisors should understand about diligence, liquidity, and valuation as these strategies move downstreamDisclosures and DisclaimersThe views expressed by the host and guest are their own and are for informational and educational purposes only. Neither the host nor guest is acting as a registered representative for any specific investment strategy, and this discussion should not be construed as a recommendation or endorsement of any investment.Support the show

Long-term investing can feel more difficult when headlines are loud and markets seem unpredictable.  What happens when investors stop reacting to daily noise and start thinking like institutions that plan decades ahead? In this episode, Robert Curtiss welcomes Chris Schelling, CAIA, Managing Director at Aksia, to explore how private markets have shaped institutional portfolios and why some individual investors may now gain access to approaches like those used by institutions, depending on account type, regulatory eligibility, and minimum investment requirements. They break down private equity, private credit, liquidity planning, diversification across vintages, and the importance of manager selection.  The conversation also touches on volatility, long-term return expectations, and what advisors and investors should look for when evaluating alternative investments. Key takeaways: How institutional investors approach private markets — and what does that mean for access and implementation for individual investors with long-term horizons and diversified portfolios Why private equity and private credit returns differ from public markets over multi-year periods The role of liquidity planning and why private investments are not truly locked up for a decade Why manager selection matters more in private markets than in public equities How simplified structures have made private investments easier for individual investors to access And more! Resources: Educational videos (bottom of the page) Connect with Chris Schelling: LinkedIn: Christopher Schelling Website: Aksia Connect with Robert Curtiss: rcurtiss@seia.com (626) 795-2944 About Robert Curtiss  LinkedIn: Robert Curtiss Facebook: Robert Curtiss SEIA LinkedIn: SEIA About Our Guest: Chris Schelling is an investor, advisor, and published author. With degrees in psychology, business, and finance, Chris is an expert at incorporating insights from behavioral finance into investment decision-making. During his 20+ year tenure in the investment industry, building portfolios, mostly focused on alternatives, Chris has met with over 4,000 managers and allocated roughly $7 billion, generating top quartile to top decile returns across hedge funds, real assets, private credit, and private equity.

OpenClaw's meteoric rise—from a weekend Claude experiment to the fastest-growing open source AI project in the world—just culminated in Peter Steinberger joining OpenAI to build the next generation of personal agents. This episode unpacks the agentic inflection point, why OpenClaw became the Schelling point for builders, what Anthropic may have fumbled, and what it means for multi-agent futures, coding models, and the broader AI power struggle. In the headlines: GPT-5.3 Codex Spark's speed play, Google's upgraded Deep Think agent, DeepSeek V4 rumors, and Anthropic's $30B raise.Want to build with OpenClaw?LEARN MORE ABOUT CLAW CAMP: https://campclaw.ai/Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology - Build, test and scale intelligent workloads faster with Rackspace AI Launchpad - ⁠⁠⁠⁠http://rackspace.com/ailaunchpad⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠https://blitzy.com/⁠Optimizely Agents in Action - Join the virtual event (with me!) free March 4 - ⁠⁠⁠⁠⁠⁠⁠⁠https://www.optimizely.com/insights/agents-in-action/⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://pod.link/1680633614⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Alter ist keine Krankheit, die man heilen kann, aber muss das Leben deshalb mit den Jahren durch immer mehr Krankheiten immer beschwerlicher werden? Prof. Jörg Schelling über Vitalität und das Geheimnis des gesunden Älterwerdens.

In this episode of The Distribution, Brandon Sedloff sits down with Chris Schelling for a deep dive into the evolving intersection of private markets, alternatives, and private wealth. Chris shares his unconventional path into investing, from studying psychology to becoming a longtime allocator and advisor across institutional and wealth channels. The conversation explores how private markets have matured, why education remains a critical gap in the wealth space, and what it really takes to allocate capital effectively in opaque and complex markets. Drawing on decades of experience meeting thousands of managers, Chris offers a clear-eyed perspective on risk, return dispersion, and the structural shifts reshaping private capital. They discuss: How psychology, behavior, and incentives shape decision-making in private markets The growing role of private wealth in alternatives and why institutional playbooks still matter Risks like adverse selection, return dispersion, and misaligned incentives in private investments Interval funds and why they can be effective tools for accessing private markets at scale Chris's outlook on private equity, private credit, hedge funds, venture, and real assets over the next two years Links: Aksia - https://www.aksia.com/ Chris on LinkedIn - https://www.linkedin.com/in/christopher-schelling/ Brandon on LinkedIn - https://www.linkedin.com/in/bsedloff/ Juniper Square - https://www.junipersquare.com/ Topics: (00:00:00) - Intro (00:03:12) - Chris' background and career (00:05:33) - The intersection of psychology and finance (00:07:30) - Understanding capital markets and alternatives (00:16:49) - The role of private markets in portfolios (00:31:15) - Understanding interval funds (00:32:36) - Managing risks and liquidity (00:34:01) - Challenges and strategies in fund management (00:35:25) - The importance of education in wealth management (00:37:33) - Adapting to market trends and client needs (00:39:55) - The role of large asset managers (00:43:43) - Private markets and wealth management (00:49:11) - Evaluating general partners (GPs) (00:52:35) - Current trends in alternative investments (01:00:23) - Conclusion and contact information

This holiday season, you'll see many charity fundraisers. I've already mentioned three, and I have another lined up for next week's open thread. Many great organizations ask me to signal-boost them, I'm happy to comply, and I'm delighted when any of you donate. Still, I used to hate this sort of thing. I'd be reading a blog I liked, then - wham, "please donate to save the starving children". Now I either have to donate to starving children, or feel bad that I didn't. And if I do donate, how much? Obviously no amount would fully reflect the seriousness of the problem. When I was a poor college student, I usually gave $10, because it was a nice round number; when I had more money, I usually gave $50, for the same reason. But then the next week, a different blog would advertise "please donate to save the starving children with cancer", and I'd feel like a shmuck for wasting my donation on non-cancerous starving children. Do I donate another $10, bringing my total up to the non-round number of $20? If I had a spare $20 for altruistic purposes, why hadn't I donated that the first time? It was all so unpleasant, and no matter what I did, I would feel all three of stingy and gullible and irrational. This is why I was so excited ten-odd years ago when I discovered the Giving What We Can Pledge. It's a commitment to give a certain percent of your income (originally 10%, but now there's also a 1-10% "trial" pledge) to the most effective charity you know. If you can't figure out which charity is most effective, you can just donate to Against Malaria Foundation, like all the other indecisive people. It's not that 10% is obviously the correct number in some deep sense. The people who picked it, picked it because it was big enough to matter, but not so big that nobody would do it. But having been picked, it's become a Schelling point. Take it, and you're one of the 10,000 people who's made this impressive commitment. If someone asks why you're not giving more, you can say "That would dilute the value of the Schelling point we've all agreed on and make it harder for other people to cooperate with us". The specific numbers and charities matter less than the way the pledge makes you think about your values and then yoke your behavior to them. In theory we're supposed to do this all the time. Another holiday institution, New Year's Resolutions, also centers around considering your values and yoking your behavior. But they famously don't work: most people don't have the willpower to go to the gym three times a week, or to volunteer at their local animal shelter on Sundays, or whatever else they decide on. That's why GWWC Pledge is so powerful. No willpower involved. Just go to your online banking portal, click click click, and you're done. Over my life, I don't know if I would say I've ever really changed my character or willpower or overall goodness/badness balance by more than a few percent. But I changed the amount I donated by a factor of ~ten, forever, with one very good decision. Unless you're a genius or a saint, your money is the strongest tool you have to change the world. 10% of an ordinary First World income donated to AMF saves dozens of lives over a career; even if you're a policeman or firefighter, you'll have trouble matching that through non-financial means. Unless you're Charlie Kirk or Heather Cox Richardson, no amount of your political activism or voting - let alone arguing on the Internet - will match the effect of donating to a politician or a cause you care about. And no amount of carpooling and eating vegan will help the climate as much as donating to carbon capture charities. Not an effective altruist? Think it's better to contribute to your local community, school, theater, or church? I'll argue with you later - but for now, my advice is the same. Have you thought really hard about how you should be contributing to your local community, school, theater, or church? (The fundraising letters my family used to get from our synagogue left little doubt about what form of contribution they preferred). Have you pledged some specific amount? You won't give beyond the $10-when-you-see-a-blog-fundraiser level unless you take a real pledge, registered by someone besides yourself - trust me, I've tested this. The GWWC website is mostly pitched at EAs. But if you like churches so much, you can probably get the same effect by pledging to God - and He keeps His own list, and offers His own member perks. To the degree that you care about changing the world beyond yourself and your family, in any direction, then the odds are good that this one decision - whether or not to take a binding charitable Pledge - matters more than every other decision you'll ever make combined. Maybe an order of magnitude more. It's something you can do right now, in five minutes. You shouldn't do it in five minutes; you should sit down and think about it hard and talk it over with your loved ones and make sure you're really planning to keep whatever pledge you make. But you could. And then every time you saw a charity fundraiser on a blog, you could think "Oh, sorry, I'm already living my life in accordance with my altruistic values, no thanks!" You wouldn't even have to worry about how much to donate. I don't even donate to half the fundraisers that I signal-boost! So if you have time this holiday season, and you're financially secure enough that it won't be a burden, think about whether there's some way you want the world to be different and better, whether there are charities that work on it, and whether you want to donate. Then, take the pledge. If you decide you want to do something but it's too stressful to figure out what, take a 3% trial pledge here, give it to Against Malaria Foundation, and come back next year to see if you're ready for the 10% version. UPDATE: Bentham's Bulldog also thinks you should take the pledge - here's his post. And I'll match his offer - take the full 10% pledge this month, and comment below so that I know about it, and I'll give you a free lifetime subscription to ACX. https://www.astralcodexten.com/p/the-pledge

Was könnte ich haben? Viele Menschen googlen erstmal nach möglichen Diagnosen oder fragen ChatGPT. Und auch Ärzte und Ärztinnen nutzen KI. Doch wie verändert künstliche Intelligenz die Medizin? Mit Hausarzt Prof. Jörg Schelling.

Die meisten Menschen möchten daheim sterben, nicht in der Klinik. Mit der modernen palliativmedizinischen Versorgung sind die Voraussetzungen gut. Der Hausarzt Prof. Jörg Schelling erklärt, was die Allgemeinmedizin beitragen kann.

Blutdruck, Cholesterinspiegel, Zuckerwerte, EKG... was bringen solche Check-ups beim Hausarzt? Zahlt die Kasse alles, was nötig und sinnvoll ist? Antworten vom Hausarzt und Prof. für Allgemeinmedizin Jörg Schelling.

Paola Capriolo"Il superfluo della vita"Carbonio Editorewww.carbonioeditore.itLa nobile Clara e il borghese Heinrich, spiriti inquieti in un mondo che non li comprende, decidono di stare insieme a dispetto di ogni regola, unendosi in un matrimonio segreto contro la volontà del padre di lei. Innamorati e felici, gli sposi vivono nascosti in un'angusta soffitta, nutrendosi di passione e sogni, assorti nella beatitudine di un dolce conversare, rinunciando al superfluo per godersi la vita nella sua poetica essenzialità. Ma l'inverno impietoso e la miseria spingono Heinrich a uno stravagante espediente che è anche un atto estremo e irrevocabile: bruciare la scala che li collega al mondo, scegliendo l'amore come unico rifugio, pur sapendo di condannarsi all'isolamento…Scritta nel 1839 e considerata dallo stesso autore una delle sue opere più riuscite, Il superfluo della vita è una novella delicata e luminosa, piena di arguzia e candore, in cui l'incanto della fiaba avvolge il mistero della vita, sospesa tra presente e passato, tra doveri e diletti, tra sogno e realtà.Ludwig Tieck (Berlino, 1773-1853) è stato un influente scrittore, traduttore, poeta e critico letterario tedesco, figura di spicco del Romanticismo. Nel 1799 diede vita insieme a Novalis, i fratelli Schlegel, Schelling e Fichte al circolo romantico di Jena, un punto di riferimento per la letteratura dell'epoca. Tra le sue opere più significative si annoverano i romanzi Storia del signor William Lovell (1796) e Le peregrinazioni di Franz Sternbald (1798), il racconto fiabesco Il biondo Eckbert (1797), le fiabe teatrali Il gatto con gli stivali (1797) e Il mondo alla rovescia (1798), le novelle Il fidanzamento (1823) e Il superfluo della vita (1839).Paola Capriolo, nata a Milano nel 1962, è autrice di numerosi libri di narrativa, da La grande Eulalia (Feltrinelli 1988) a Irina Nikolaevna o l'arte del romanzo (Bompiani 2023). Le sue opere sono tradotte in molti Paesi. Ha scritto saggi su Benn, Rilke e Thomas Mann e tradotto per diversi editori testi di Goethe, Kleist, Keller, Stifter, Schnitzler, Thomas Mann e Kafka. Dal 2018 fa parte della giuria del Premio italo-tedesco per la traduzione letterariaDiventa un supporter di questo podcast: https://www.spreaker.com/podcast/il-posto-delle-parole--1487855/support.IL POSTO DELLE PAROLEascoltare fa pensarehttps://ilpostodelleparole.it/

Die Abnehmspritze: Sie wirkt nicht bei allen und wer sie absetzt, nimmt schnell wieder zu. Wie sinnvoll ist sie also für Diabetiker? Und kann moderne Technik vielleicht besser helfen? Mit Allgemeinarzt Prof. Jörg Schelling.

Bei den einen ist es die Laktose, andere vertragen kein Histamin, keine Fruktose, keine Vollkornprodukte oder keine Zusatzstoffe. Prof. Jörg Schelling sucht mit den Anrufenden nach Wegen, wie Essen dennoch wieder Spaß macht.

To celebrate the 10 Year Anniversary of the Outdoor Product Design & Development program at Utah State University, we are sharing conversations with alumni, faculty, and industry! Enjoy this conversation with OPDD alum and current Associate Footwear Designer at Xero Shoes, Ciera Schelling. Listen to these conversations on the Highlander Podcast. https://opdd.usu.edu/podcast The Highlander Podcast is sponsored by the Outdoor Product Design & Development program at Utah State University, a four-year, undergraduate degree training the next generation of product creators for the sports and outdoor industries. Learn more at opdd.usu.edu or follow the program on LinkedIn or Instagram. https://www.instagram.com/usuoutdoorproduct/ https://www.linkedin.com/company/opdd Discover the Outdoor Recreation Archive on Instagram or on USU's website. https://instagram.com/outdoorrecarchive https://library.usu.edu/archives/ora Subscribe to our ORA newsletter: https://outdoorrecarchive.substack.com/ Outdoor Recreation Archive Instagram https://www.instagram.com/outdoorrecarchive/?hl=en Episodes hosted, edited, and produced by Chase Anderson in beautiful Cache Valley, Utah. https://www.linkedin.com/in/chasewoodruffanderson/

Steven Pinker returns to Conversations with Tyler with an argument that common knowledge—those infinite loops of "I know that you know that I know"—is the hidden infrastructure that enables human coordination, from accepting paper money to toppling dictators. But Tyler wonders: if most real-world coordination works fine without recursively looping (a glance at a traffic circle), if these models break down with the slightest change in assumptions, and if anonymous internet posters are making correct but uncomfortable truths common knowledge when society might function better with noble lies, is Pinker's theory really capturing how coordination works—and might we actually need less common knowledge, not more? Tyler and Steven probe these dimensions of common knowledge—Schelling points, differential knowledge, benign hypocrisies like  a whisky bottle in a paper bag—before testing whether rational people can actually agree (spoiler: they can't converge on Hitchcock rankings despite Aumann's theorem), whether liberal enlightenment will reignite and why, what stirring liberal thinkers exist under the age 55, why only a quarter of Harvard students deserve A's, how large language models implicitly use linguistic insights while ignoring linguistic theory, his favorite track on Rubber Soul, what he'll do next, and more. Read a full transcript enhanced with helpful links, or watch the full video on the new dedicated Conversations with Tyler channel. Recorded September 12th, 2025. This episode was made possible through the support of the John Templeton Foundation. Other ways to connect Follow us on X and Instagram Follow Tyler on X Follow Steven on X Sign up for our newsletter Join our Discord Email us: cowenconvos@mercatus.gmu.edu Learn more about Conversations with Tyler and other Mercatus Center podcasts here.

Een heel klein beetje over de etappe, maar vooral over de soap bij UAE. Hoe heeft het zo kunnen escaleren tussen Juan Ayuso en de ploeg? En waarom moest het statement over het ontbinden van zijn contract midden in de Vuelta naar buiten worden gebracht? Niek Goedvolk bespreekt het in een nieuwe aflevering van In Het Wiel met Roxane Knetemann en Daniël Dwarswaard. Uiteraard ook aandacht voor de Simac Ladies Tour en het stoppen van Ide Schelling. En vergeet niet je ideeën voor het hervormen van de grote rondes naar ons in te sturen via Instagram! See omnystudio.com/listener for privacy information.

Kelsie Schelling drove from Denver to Pueblo, Colorado, to meet her boyfriend after revealing she was pregnant. Her car was found abandoned and she was never seen again. Get exclusive Killer Instinct content on my patreon : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/killerinstinct ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you like the show, telling a friend about it would be helpful! You can text, email, Tweet, or send this link to a friend: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/KillerInstinctPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Savannah on IG: @savannahbrymer Follow Savannah on Twitter: @savannahbrymer Get exclusive Killer Instinct content on my patreon : ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.patreon.com/killerinstinct ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ If you like the show, telling a friend about it would be helpful! You can text, email, Tweet, or send this link to a friend: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠http://bit.ly/KillerInstinctPod⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Follow Savannah on IG: @savannahbrymer Follow Savannah on Twitter: @savannahbrymer Learn more about your ad choices. Visit podcastchoices.com/adchoices

Matthew David Segall, PhD, is an Associate Professor in the Philosophy, Cosmology, and Consciousness Department at California Institute of Integral Studies in San Francisco, and the Chair of the Science Advisory Committee for the Cobb Institute. He is a transdisciplinary researcher, writer, teacher, and philosopher applying process-relational thought across the natural and social sciences, as well as to the study of consciousness. He describes himself as a “process philosopher” and transdisciplinary researcher, reflecting his commitment to bridging multiple fields​. Segall's work builds on the metaphysical framework of Alfred North Whitehead, extending Whitehead's philosophy of organism into new domains of science, religion, and ecology. In doing so, Segall reinterprets the Western philosophical lineage – from ancient ideas of a world-soul to German Idealism and beyond – to articulate a participatory, organismic vision of nature. His philosophy portrays a cosmos ensouled with meaning and experience, challenging mechanistic materialism and inviting a renewed dialogue between science and spirit​. Segall integrates insights from Whitehead, Schelling, Goethe, and Steiner into a process worldview, develops an organic (panpsychist) cosmology, practices a bold transdisciplinary methodology, and engages public dialogues that embody a form of sacred activism on behalf of our living planet.TIMESTAMPS:(0:00) - Introduction (0:43) - History of Mind-Body Problem(7:40) - Critiquing Physicalism(12:55) - Quantum Theory Interpretations(16:14) - Addressing Illusionism & Scientism(22:00) - The Metaphysics of Prehension(28:14) - Panexperientialism in Physics(31:55) - Propositional Feelings(37:09) - What is Consciousness?(45:00) - Panexperientialism & Free Will(50:00) - Bridging Science & Philosophy(54:42) - Challenging the Cold/Dead Universe tale(1:00:39) - Misconceptions about Matt's work(1:04:20) - Telos(1:07:44) - Matt's Philosopher recommendations(1:13:00) - Mind At Large (Upcoming Events!)(1:17:40) - Conclusion EPISODE LINKS:- Matt's Website: https://footnotes2plato.com-  @Footnotes2Plato : http://www.youtube.com/@Footnotes2Plato- Physics Within the Bounds of Feeling Alone: https://footnotes2plato.com/wp-content/uploads/2023/11/physics-within-the-bounds-of-feeling-alone.pdf- Matt's X: https://x.com/ThouArtThat- Matt's Facebook: https://www.facebook.com/matthew.david.segall- Matt's LinkedIn: https://www.linkedin.com/in/matthewdavidsegall- Matt's Instagram: https://www.instagram.com/footnotes2platoCONNECT:- Website: https://tevinnaidu.com - Podcast: https://creators.spotify.com/pod/show/mindbodysolution- YouTube: https://youtube.com/mindbodysolution- Twitter: https://twitter.com/drtevinnaidu- Facebook: https://facebook.com/drtevinnaidu - Instagram: https://instagram.com/drtevinnaidu- LinkedIn: https://linkedin.com/in/drtevinnaidu=============================Disclaimer: The information provided on this channel is for educational purposes only. The content is shared in the spirit of open discourse and does not constitute, nor does it substitute, professional or medical advice. We do not accept any liability for any loss or damage incurred from you acting or not acting as a result of listening/watching any of our contents. You acknowledge that you use the information provided at your own risk. Listeners/viewers are advised to conduct their own research and consult with their own experts in the respective fields.

This episode was recorded live at Schelling Point during Eth Denver 2025. In our fourth Rehash Hot Ones show, competitors Kevin Owocki, Disruption Joe, Sophia Dew, and Rena O'Brien battle it out for who has the spiciest takes on DAO governance, web3 funding, and the potential for AI to address challenges in crypto. Along the way, they brave increasingly spicy hot sauces, and audience members participate with their own hot takes as well while also engaging in the interactive spice challenge on stage. Finally, votes are tallied live on JokeRace and a winner is crowned. ⏳ TIMESTAMPS: 0:00 Intro 01:57 Panelist introductions 03:10 Rules and voting 04:28 Round 1: Does DAO Governance Work? 18:13 Round 2: Best Way to Fund Web3 Projects 27:43 Round 3: Can AI Fix Crypto? 37:35 Winner announced 

Jim talks with Adam B. Levine about AI programming aids for non-techies and the future of Bitcoin. They discuss Adam's background as a "technical non-technical" person, the evolution from manual LLM prompting to using IDEs, Windsurf as an AI-first IDE, Claude 3.7's thinking mode, productivity improvements with AI coding tools, different platforms like Cursor and Cline, the "pure idea space" vs technical execution, the role of liberal arts people in tech teams, Bitcoin as digital gold, Schelling points in cryptocurrency, the US dollar as hegemonic currency, "pools of fools" theory, sovereign wealth funds moving into Bitcoin, El Salvador's Bitcoin investment, Texas and Wyoming considering sovereign Bitcoin funds, game theory of nation-state Bitcoin adoption, regulatory transitions, predictions about Bitcoin's future based on sovereign adoption, and much more. Episode Transcript The Diamond Age: Or, a Young Lady's Illustrated Primer, by Neal Stephenson Speaking of Bitcoin Podcast (formerly Let's Talk Bitcoin!) Adam B. Levine has spent over a decade pioneering disruptive technologies before they become mainstream. He launched one of the earliest Bitcoin podcasts, Let's Talk Bitcoin! (2013), founded Tokenly (2014)—one of the earliest companies exploring what could be done with blockchain tokens—and served as CoinDesk's first podcast editor (2019), hosting shows like Speaking of Bitcoin and Markets Daily. In 2021, he founded 330.ai, a startup building cutting-edge tools to boost creativity with AI.

In February of 2013, a 21-year-old woman mysteriously disappeared during a visit to see her on-again, off-again boyfriend after he told her he had a “surprise gift” for her. A subsequent investigation uncovered strange text messages, perplexing surveillance footage, and an important insider tip from an unsuspecting witness. This is the story of Kelsie Schelling.

Today we talk about the relationship between philosophy and religion. We talk about the duck-rabbit as a metaphor that may have something useful to teach us about the way we experience reality. We talk about the enormous difficulty of fully addressing the question: what is religion? We talk about Schelling's historical view of revelation and its connection to a possible new era of Christian religious practice. Hope you love it! :) Sponsors: Harry's: https://www.harrys.com/PHILOSOPHIZE Nord VPN: https://nordvpn.com/philothis Thank you so much for listening! Could never do this without your help.  Website: https://www.philosophizethis.org/ Patreon: https://www.patreon.com/philosophizethis  Social: Instagram: https://www.instagram.com/philosophizethispodcast X: https://twitter.com/iamstephenwest Facebook: https://www.facebook.com/philosophizethisshow Learn more about your ad choices. Visit podcastchoices.com/adchoices

As a listener of TOE you can get a special 20% off discount to The Economist and all it has to offer! Visit https://www.economist.com/toe In today's episode of Theories of Everything, Curt Jaimungal speaks with Matthew Segall, a professor at the California Institute of Integral Studies, on the evolution of philosophical thought, linking ancient teachings on consciousness to modern scientific perspectives. We delve into the limitations of contemporary views of reality, paralleling them with the Ptolemaic model, and explore how an awareness of mortality can enrich our understanding of existence. Matthew argues for a shift toward introspection and self-inquiry in a society grappling with existential challenges, emphasizing that confronting mortality can foster a deeper sense of meaning in our lives. New Substack! Follow my personal writings and EARLY ACCESS episodes here: https://curtjaimungal.substack.com LINKED MENTIONED: •⁠ ⁠Matthew's YouTube channel: https://www.youtube.com/@Footnotes2Plato •⁠ ⁠Matthew's Diagram of Hegel's Phenomenology of Spirit: https://www.youtube.com/watch?v=9Z1zY39EKbs •⁠ ⁠Matthew's talk with John Vervaeke: https://www.youtube.com/watch?v=15akhXGHwzo •⁠ ⁠Critique of Pure Reason (book): https://www.amazon.com/Critique-Pure-Reason-Penguin-Classics/dp/0140447474 •⁠ ⁠Critique of Judgement (book): https://www.amazon.com/Critique-Judgement-Immanuel-Kant/dp/1545245673/ref=tmm_pap_swatch_0?_encoding=UTF8&qid=&sr= •⁠ ⁠The Phenomenology of Spirit (book): https://www.amazon.com/Georg-Wilhelm-Friedrich-Hegel-Phenomenology/dp/1108730086 •⁠ ⁠1919 Eclipse (paper): https://royalsocietypublishing.org/doi/epdf/10.1098/rsnr.2020.0040 •⁠ ⁠Einstein/Bergson debate (article): https://www.faena.com/aleph/einstein-vs-bergson-the-struggle-for-time •⁠ ⁠The Principle of Relativity (book): https://www.amazon.com/Principle-Relativity-Alfred-North-Whitehead/dp/1602062188 •⁠ ⁠John Vervaeke's YouTube channel: https://www.youtube.com/@johnvervaeke •⁠ ⁠John Vervaeke on TOE: https://www.youtube.com/watch?v=GVj1KYGyesI •⁠ ⁠Philip Goff on TOE: https://www.youtube.com/watch?v=MmaIBxkqcT4 •⁠ ⁠Sabine Hossenfelder on TOE: https://www.youtube.com/watch?v=E3y-Z0pgupg •⁠ ⁠Donald Hoffman on TOE: https://www.youtube.com/watch?v=CmieNQH7Q4w •⁠ ⁠Karl Friston on TOE: https://www.youtube.com/watch?v=uk4NZorRjCo •⁠ ⁠Iain McGilchrist on TOE: https://www.youtube.com/watch?v=Q9sBKCd2HD0 •⁠ ⁠Thomas Campbell on TOE: https://www.youtube.com/watch?v=kko-hVA-8IU •⁠ ⁠Noam Chomsky on TOE: https://www.youtube.com/watch? •⁠ ⁠v=3lcDT_-3v2k&list=PLZ7ikzmc6zlORiRfcaQe8ZdxKxF-e2BCY&index=3 •⁠ ⁠Michael Levin on TOE: https://www.youtube.com/watch?v=c8iFtaltX-s&list=PLZ7ikzmc6zlN6E8KrxcYCWQIHg2tfkqvR&index=39 •⁠ ⁠Roger Penrose on TOE: https://www.youtube.com/watch?v=sGm505TFMbU&list=PLZ7ikzmc6zlN6E8KrxcYCWQIHg2tfkqvR&index=16 •⁠ ⁠Neil Turok's lecture on TOE: https://www.youtube.com/watch?v=-gwhqmPqRl4&list=PLZ7ikzmc6zlN6E8KrxcYCWQIHg2tfkqvR&index=35 •⁠ ⁠TOE's Consciousness Iceberg: https://www.youtube.com/watch?v=TR4cpn8m9i0&ab_channel=TheoriesofEverythingwithCurtJaimungal •⁠ ⁠TOE's String Theory Iceberg: https://www.youtube.com/watch?v=X4PdPnQuwjY Timestamps: 00:00 Introduction 1:35 The Roots of Process Philosophy 4:47 The Rise of Nominalism 8:26 The Evolution of Substance 11:02 Descartes and the Dualist Divide 21:34 Kant's Copernican Revolution 33:08 The Nature of Knowledge 37:42 Hegel's Dialectic Unfolds 46:18 Schelling's Panpsychism 56:50 Whitehead's Organic Realism 1:22:17 The Bifurcation of Nature 1:31:38 The Emergence of Consciousness 1:38:37 The Nature of Self-Organization 1:53:40 Perspectives on Actuality and Potentiality 2:11:35 The Role of God in Process Philosophy 2:23:55 The Human Experience and Self-Inquiry 2:40:34 Reflections on Mortality and Meaning 2:47:44 The Shift from Substance to Process 2:58:02 Embracing Interconnectedness and Consciousness 3:00:49 The Call for Inner Exploration #science #philosophy Learn more about your ad choices. Visit megaphone.fm/adchoices

Christopher Satoor is a doctoral candidate (ABD) in the Department of Humanities at York University. His research focuses on Classical German philosophy of the 18th and 19th-century and the German idealist philosophies of Kant and Fichte, with an extra special concentration on Friedrich Wilhelm Joseph Schelling. Site: https://philpeople.org/profiles/christopher-satoor Satoor's podcast: https://www.youtube.com/ @TheYoungIdealist  ---Become part of the Hermitix community:Hermitix Twitter - / hermitixpodcast Support Hermitix:Patreon - / hermitix Donations: - https://www.paypal.me/hermitixpodHermitix Merchandise - http://teespring.com/stores/hermitix-2Bitcoin Donation Address: 3LAGEKBXEuE2pgc4oubExGTWtrKPuXDDLKEthereum Donation Address: 0x31e2a4a31B8563B8d238eC086daE9B75a00D9E74